Effective semantic features for facial expressions recognition using SVM

[[abstract]]Most traditional facial expression-recognition systems track facial components such as eyes, eyebrows, and mouth for feature extraction. Though some of these features can provide clues for expression recognition, other finer changes of the facial muscles can also be deployed for classifying various facial expressions. This study locates facial components by active shape model to extract seven dynamic face regions (frown, nose wrinkle, two nasolabial folds, two eyebrows, and mouth). Proposed semantic facial features could then be acquired using directional gradient operators like Gabor filters and Laplacian of Gaussian. A multi-class support vector machine (SVM) was trained to classify six facial expressions (neutral, happiness, surprise, anger, disgust, and fear). The popular Cohn–Kanade database was tested and the average recognition rate reached 94.7 %. Also, 20 persons were invited for on-line test and the recognition rate was about 93 % in a real-world environment. It demonstrated that the proposed semantic facial features could effectively represent changes between facial expressions. The time complexity could be lower than the other SVM based approaches due to the less number of deployed features.[[notice]]補正完

Product of natural evolution (SARS, MERS, and SARS-CoV-2); deadly diseases, from SARS to SARS-CoV-2

SARS-CoV-2, the virus causing COVID-19, is a single-stranded RNA virus belonging to the order Nidovirales, family Coronaviridae, and subfamily Coronavirinae. SARS-CoV-2 entry to cellsis initiated by the binding of the viral spike protein (S) to its cellular receptor. The roles of S protein in receptor binding and membrane fusion makes it a prominent target for vaccine development. SARS-CoV-2 genome sequence analysis has shown that this virus belongs to the beta-coronavirus genus, which includes Bat SARS-like coronavirus, SARS-CoV and MERS-CoV. A vaccine should induce a balanced immune response to elicit protective immunity. In this review, we compare and contrast these three important CoV diseases and how they inform on vaccine development

A community effort to create standards for evaluating tumor subclonal reconstruction

Methods for reconstructing tumor evolution are benchmarked in the DREAM Somatic Mutation Calling Tumour Heterogeneity Challenge. Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity